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Effects of Disruption Risks on Biorefinery Location Design

Author

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  • Yun Bai

    (Center for Advanced Infrastructure and Transportation, Rutgers, the State University of New Jersey, 100 Brett Road, Piscataway, NJ 08854-8058, USA)

  • Xiaopeng Li

    (Department of Civil and Environmental Engineering, Mississippi State University, 235L Walker Engineering Building, Starkville, MS 39762-9546, USA)

  • Fan Peng

    (Operations Research Department, CSX Transportation, Inc., Jacksonville, FL 32202, USA)

  • Xin Wang

    (Department of Civil and Environmental Engineering, University of Illinois at Urbana-Champaign, 205 N. Mathews Ave., Urbana, IL 61801, USA)

  • Yanfeng Ouyang

    (Department of Civil and Environmental Engineering, University of Illinois at Urbana-Champaign, 205 N. Mathews Ave., Urbana, IL 61801, USA)

Abstract

While ever-growing bio-ethanol production poses considerable challenges to the bioenergy supply chain, the risk of refinery operation disruptions further compromises the efficiency and reliability of the energy supply system. This paper applies discrete and continuous reliable facility location models to the design of reliable bio-ethanol supply chains so that the system can hedge against potential operational disruptions. The discrete model is shown to be suitable for obtaining the exact optimality for small or moderate instances, while the continuous model has superior computational tractability for large-scale applications. The impacts of both site-independent and dependent disruptions ( i.e. , due to flooding) are analyzed in empirical case study for the State of Illinois (one of the main biomass supply states in the U.S.). The reliable solution is compared with a deterministic solution under the same setting. It is found that refinery disruptions, especially those site-dependent ones, affect both optimal refinery deployment and the supply chain cost. Sensitivity analysis is also conducted to show how refinery failure probability and fixed cost (for building biorefineries) affect optimal supply chain configuration and the total expected system cost.

Suggested Citation

  • Yun Bai & Xiaopeng Li & Fan Peng & Xin Wang & Yanfeng Ouyang, 2015. "Effects of Disruption Risks on Biorefinery Location Design," Energies, MDPI, vol. 8(2), pages 1-19, February.
    • Handle: RePEc:gam:jeners:v:8:y:2015:i:2:p:1468-1486:d:45884
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    References listed on IDEAS

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    3. Yazdanparast, R. & Jolai, F. & Pishvaee, M.S. & Keramati, A., 2022. "A resilient drop-in biofuel supply chain integrated with existing petroleum infrastructure: Toward more sustainable transport fuel solutions," Renewable Energy, Elsevier, vol. 184(C), pages 799-819.
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    5. Zhixue Liu & Shukun Wang & Yanfeng Ouyang, 2017. "Reliable Biomass Supply Chain Design under Feedstock Seasonality and Probabilistic Facility Disruptions," Energies, MDPI, vol. 10(11), pages 1-18, November.
    6. Wang, Xin & Lim, Michael K. & Ouyang, Yanfeng, 2015. "Infrastructure deployment under uncertainties and competition: The biofuel industry case," Transportation Research Part B: Methodological, Elsevier, vol. 78(C), pages 1-15.
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    8. Sushil Poudel & Mohammad Marufuzzaman & Md Abdul Quddus & Sudipta Chowdhury & Linkan Bian & Brian Smith, 2018. "Designing a Reliable and Congested Multi-Modal Facility Location Problem for Biofuel Supply Chain Network," Energies, MDPI, vol. 11(7), pages 1-24, June.
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    10. Gital, Yeşim & Bilgen, Bilge, 2024. "Resilient strategies for managing supply and facility disruptions in a biomass supply chain," Applied Energy, Elsevier, vol. 372(C).
    11. Ansari, Sina & Başdere, Mehmet & Li, Xiaopeng & Ouyang, Yanfeng & Smilowitz, Karen, 2018. "Advancements in continuous approximation models for logistics and transportation systems: 1996–2016," Transportation Research Part B: Methodological, Elsevier, vol. 107(C), pages 229-252.
    12. Yun, Lifen & Wang, Xifu & Fan, Hongqiang & Li, Xiaopeng, 2020. "Reliable facility location design with round-trip transportation under imperfect information Part I: A discrete model," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 133(C).
    13. Cui, Jianxun & Zhao, Meng & Li, Xiaopeng & Parsafard, Mohsen & An, Shi, 2016. "Reliable design of an integrated supply chain with expedited shipments under disruption risks," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 95(C), pages 143-163.
    14. Salimi, F. & Vahdani, Behnam, 2018. "Designing a bio-fuel network considering links reliability and risk-pooling effect in bio-refineries," Reliability Engineering and System Safety, Elsevier, vol. 174(C), pages 96-107.
    15. Lifen Yun & Xifu Wang & Hongqiang Fan & Xiaopeng Li, 2017. "A reliable facility location design model with site-dependent disruption in the imperfect information context," PLOS ONE, Public Library of Science, vol. 12(5), pages 1-18, May.
    16. Poudel, Sushil Raj & Marufuzzaman, Mohammad & Bian, Linkan, 2016. "A hybrid decomposition algorithm for designing a multi-modal transportation network under biomass supply uncertainty," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 94(C), pages 1-25.
    17. Aldrighetti, Riccardo & Battini, Daria & Ivanov, Dmitry & Zennaro, Ilenia, 2021. "Costs of resilience and disruptions in supply chain network design models: A review and future research directions," International Journal of Production Economics, Elsevier, vol. 235(C).

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